Downloading from Shiny

downloadButton()

is a special UI input widget designed to launch a download window from your Shiny app.

downloadButton() is a special case of an actionButton() with specialized server syntax. These are different from the other inputs we’ve used this far as they are primarily used to trigger an action rather than return a value.

Rather than using an observe() or render*(), this widget is paired with the special downloadHandler() function which uses the latter’s syntax in our server function.

downloadHandler()

Specifically, within our server definition we attach the downloadHandler() to the downloadButton’s id via output, e.g.

output$download_btn = downloadHandler(...)

The handler then defines:

  • a filename function for generating a default filename and

  • a content function for writing the download file’s content to a temporary file

Demo 05 - A download button

demos/demo05.R

library(tidyverse)
library(shiny)
library(bslib)

d = readr::read_csv(here::here("data/weather.csv"))

d_vars = c(
  "Average temp" = "temp_avg",   "Min temp"       = "temp_min",
  "Max temp"     = "temp_max",   "Total precip"   = "precip",
  "Snow depth"   = "snow",       "Wind direction" = "wind_direction",
  "Wind speed"   = "wind_speed", "Air pressure"   = "air_press"
)

ui = page_sidebar(
  title = "Weather Forecasts",
  sidebar = sidebar(
    selectInput(
      "region", "Select a region",
      choices = sort(unique(d$region)),
      selected = "West"
    ),
    selectInput(
      "name", "Select an airport",
      choices = c()
    ),
    selectInput(
      "var", "Select a variable",
      choices = d_vars, selected = "temp_avg"
    ),
    downloadButton("download")
  ),
  plotOutput("plot")
)

server = function(input, output, session) {
  output$download = downloadHandler(
    filename = function() {
      name = input$name |>
          stringr::str_replace_all(" ", "_") |>
          tolower()
      paste0(name, ".csv")
    },
    content = function(file) {
      readr::write_csv(d_city(), file)
    }
  )
  
  d_city = reactive({
    req(input$name)
    d |>
      filter(name %in% input$name)
  })
  
  observe({
    updateSelectInput(
      session, "name",
      choices = d |>
        distinct(region, name) |>
        filter(region == input$region) |>
        pull(name)
    )
  })
  
  output$plot = renderPlot({
    d_city() |>
      ggplot(aes(x=date, y=.data[[input$var]])) +
      ggtitle(input$var) +
      geom_line() +
      theme_minimal()
  })
}

shinyApp(ui = ui, server = server)

Controlling the reactive graph

For both observers and reactive expressions Shiny will automatically determine reactive dependencies for you - in some cases this is not what we want.

To explicitly control the dependencies of reactive expressions, render functions, and observers we can modify them using bindEvent() where the dependencies are explicitly listed

Similar effects can be achieved via observeEvent() / eventReactive() but these have been soft deprecated as of Shiny 1.6.

Demo 06 - A fancier download experience

demos/demo06.R

library(tidyverse)
library(shiny)
library(bslib)

d = readr::read_csv(here::here("data/weather.csv"))

d_vars = c(
  "Average temp" = "temp_avg",   "Min temp"       = "temp_min",
  "Max temp"     = "temp_max",   "Total precip"   = "precip",
  "Snow depth"   = "snow",       "Wind direction" = "wind_direction",
  "Wind speed"   = "wind_speed", "Air pressure"   = "air_press"
)

ui = page_sidebar(
  title = "Weather Forecasts",
  sidebar = sidebar(
    selectInput(
      "region", "Select a region",
      choices = sort(unique(d$region)),
      selected = "West"
    ),
    selectInput(
      "name", "Select an airport",
      choices = c()
    ),
    selectInput(
      "var", "Select a variable",
      choices = d_vars, selected = "temp"
    ),
    actionButton("export_modal", "Export data")
  ),
  plotOutput("plot")
)

server = function(input, output, session) {
  
  observe({
    showModal( modalDialog(
      title = "Download data",
      dateRangeInput(
        "dl_dates", "Select date range",
        start = min(d_city()$date), end = max(d_city()$date)
      ),
      checkboxGroupInput(
        "dl_vars", "Select variables to download",
        choices = names(d), selected = names(d), inline = TRUE
      ),
      footer = list(
        downloadButton("download"),
        modalButton("Cancel")
      )
    ) )
  }) |>
    bindEvent(input$export_modal)
  
  output$download = downloadHandler(
    filename = function() {
      name = input$name |>
          stringr::str_replace_all(" ", "_") |>
          tolower()
      paste0(name, ".csv")
    },
    content = function(file) {
      readr::write_csv(
        d_city() |>
          filter(date >= input$dl_dates[1] & date <= input$dl_dates[2]) |>
          select(input$dl_vars), 
        file
      )
    }
  )
  
  d_city = reactive({
    req(input$name)
    d |>
      filter(name %in% input$name)
  })
  
  observe({
    updateSelectInput(
      inputId = "name", 
      choices = d |>
        filter(region == input$region) |>
        pull(name) |>
        unique() |>
        sort()
    )
  })
  
  output$plot = renderPlot({
    d_city() |>
      ggplot(aes(x=date, y=.data[[input$var]])) +
      ggtitle(input$var) +
      geom_line() +
      theme_minimal()
  })
}

shinyApp(ui = ui, server = server)

Uploading to Shiny

fileInput() widget

This widget behaves a bit differently than the others we have seen - like the other widgets it returns a value via input$<id> but the value returned changes based on whether or not a file has been uploaded. Specifically, before the file is uploaded, the input will return NULL. After file(s) are uploaded the input returns a data frame with one row per file and the following columns:

  • name - the original filename (from the client’s system)

  • size - file size in bytes

  • type - file mime type, usually determined by the file extension

  • datapath - location of the temporary file on the server

Your app is then responsible for reading in and processing the uploaded file(s) as needed.

Using fileInput()

library(tidyverse)
library(shiny)
library(bslib)

ui = page_fluid(
  fileInput("upload", "Upload a file", accept = ".csv"),
  h3("Result"),
  tableOutput("result"),
  h3("Content:"),
  tableOutput("data")
)

server = function(input, output, session) {
  output$result = renderTable({
    req(input$upload)
    input$upload
  })
  
  output$data = renderTable({
    req(input$upload)
    ext = tools::file_ext(input$upload$datapath)
    
    validate(
      need(ext == "csv", "Please upload a csv file")
    )
    
    readr::read_csv(input$upload$datapath)
  })
}

shinyApp(ui = ui, server = server)

fileInput() hints

  • input$upload will default to NULL when the app is loaded, using req(input$upload) for downstream consumers prevents errors/warnings until a file is uploaded

  • Files in datapath are temporary and should be treated as ephemeral

    • additional uploads can result in the previous files being deleted

  • type is at best a guess - validate uploaded files and write defensive code

  • The accept argument helps to limit file types but cannot prevent bad uploads

Your turn - Exercise 05

Starting with the code in exercises/ex05.R replace the preloading of the weather data (d) with a reactive() version that is populated via a fileInput() widget.

You should then be able to get the same app behavior as before once data/weather.csv is uploaded. You can also check that your app works with the data/portland.csv dataset as well.

Hint - remember that anywhere that uses either d will now need to use d() instead.

12:00

Modern UIs with bslib

Shiny & bootstrap

Much of the interface provided by Shiny is based on the html elements, styling, and javascript provided by the Bootstrap library.

Knowing the specifics of html (and Bootstrap specifically) are not needed for working with Shiny - but understanding some of its conventions goes a long way to helping you customize the elements of your app (via custom CSS and other tools).

This is not the only place that Bootstrap shows up in the R ecosystem - both RMarkdown and Quarto html documents use Bootstrap for styling as well.

bslib

The bslib R package provides a modern UI toolkit for Shiny, R Markdown, and Quarto based on Bootstrap.

It provides,

  • Custom theming of Shiny apps and R Markdown documents

  • Switch between different versions of Bootstrap

  • Modern UI components like cards, value boxes, sidebars, and more.

This last set of features is what we will focus on now, with more on the first two after the break.

Cards

Cards are a UI element that you will recognize from many modern websites. They are rectangular containers with borders and padding that are used to group related information. When utilized properly to group related information, they help users better digest, engage, and navigate through content

card(
  card_header(
    "A header"
  ),
  card_body(
    shiny::markdown(
      "Some **bold** text"
    )
  )
)

Styling cards

Cards can be styled using the class argument, this is used to apply Bootstrap classes to the card and its components.

card(
  max_height = 250,
  card_header(
    "Long scrollable text",
    class = "bg-primary"
  ),
  card_body(
    lorem::ipsum(paragraphs = 3, sentences = 5),
    class = "bg-info"
  )
)

Multiple card bodies

Cards are also super flexible and can contain multiple card_body() elements. This can be useful for creating complex layouts.

card(
  max_height = 450,
  card_header(
    "Text and a map!",
    class = "bg-dark"
  ),
  card_body(
    max_height = 200, 
    class = "p-0",
    leaflet::leaflet() |>
      leaflet::addTiles()
  ),
  card_body(
    lorem::ipsum(
      paragraphs = 1, 
      sentences = 3
    )
  )
)

Value boxes

Value boxes are the other major UI component provided by bslib. They are a simple way to display a value and a label in a styled box. They are often used to display key metrics in a dashboard.

value_box(
  title = "1st value",
  value = 123,
  showcase = bs_icon("bar-chart"),
  theme = "primary",
  p("The 1st detail")
)

value_box(
  title = "2nd value",
  value = 456,
  showcase = bs_icon("graph-up"),
  theme = "secondary",
  p("The 2nd detail"),
  p("The 3rd detail")
)

Demo 07 - Shiny + Cards

demos/demo07.R

library(tidyverse)
library(shiny)
library(bslib)

d = readr::read_csv(here::here("data/weather.csv"))

d_vars = c(
  "Average temp" = "temp_avg",   "Min temp"       = "temp_min",
  "Max temp"     = "temp_max",   "Total precip"   = "precip",
  "Snow depth"   = "snow",       "Wind direction" = "wind_direction",
  "Wind speed"   = "wind_speed", "Air pressure"   = "air_press"
)

ui = page_sidebar(
  title = "Weather Data",
  sidebar = sidebar(
    selectInput(
      "region", "Select a region", 
      choices = c("West", "Midwest", "Northeast", "South")
    ),
    selectInput(
      "name", "Select an airport", choices = c()
    ),
    selectInput(
      "var", "Select a variable",
      choices = d_vars, selected = "temp_avg"
    )
  ),
  card(
    card_header(
      textOutput("title")
    ),
    card_body(
      plotOutput("plot")
    )
  )
)


server = function(input, output, session) {
  observe({
    updateSelectInput(
      session, "name",
      choices = d |>
        distinct(region, name) |>
        filter(region == input$region) |>
        pull(name)
    )
  })
  
  output$title = renderText({
    names(d_vars)[d_vars==input$var]
  })
  
  d_city = reactive({
    req(input$name)
    d |>
      filter(name %in% input$name)
  })
  
  output$plot = renderPlot({
    d_city() |>
      ggplot(aes(x=date, y=.data[[input$var]])) +
      geom_line() +
      theme_minimal()
  })
}

shinyApp(ui = ui, server = server)

Dynamic UIs

Adding value boxes

Previously we had included a table that showed minimum and maximum temperatures - lets try presenting these using value boxes instead.

Before we get to the code lets think a little bit about how we might do this:

value_box(
  title = "Average Temp",
  value = textOutput("avgtemp"),
  showcase = bsicons::bs_icon("thermometer-half"),
  theme = "success"
)

Any one see a potential issue with this?

Each value box shows a dynamic value that is calculated from the data - so we need a textOutput() and corresponding renderText() for each value box, and even more if we want to change the color or icon based on the value.

uiOutput() and renderUI()

To handle situations like this Shiny provides the ability to dynamically generate UI elements entirely within the server function.

For our case we can create all of the value boxes we need in a single renderUI() call making our code simpler and more maintainable.

Additionally, since renderUI() is a reactive context we can perform all of our calculations in the same place .

Demo 08 - Value boxes

demos/demo08.R

library(tidyverse)
library(shiny)
library(bslib)

d = readr::read_csv(here::here("data/weather.csv"))

d_vars = c(
  "Average temp" = "temp_avg",   "Min temp"       = "temp_min",
  "Max temp"     = "temp_max",   "Total precip"   = "precip",
  "Snow depth"   = "snow",       "Wind direction" = "wind_direction",
  "Wind speed"   = "wind_speed", "Air pressure"   = "air_press"
)

ui = page_sidebar(
  title = "Weather Data",
  sidebar = sidebar(
    selectInput(
      "region", "Select a region", 
      choices = c("West", "Midwest", "Northeast", "South")
    ),
    selectInput(
      "name", "Select an airport", choices = c()
    ),
    selectInput(
      "var", "Select a variable",
      choices = d_vars, selected = "temp_avg"
    )
  ),
  card(
    card_header(
      textOutput("title")
    ),
    card_body(
      plotOutput("plot")
    )
  ),
  uiOutput("valueboxes")
)

server = function(input, output, session) {
  observe({
    updateSelectInput(
      session, "name",
      choices = d |>
        distinct(region, name) |>
        filter(region == input$region) |>
        pull(name)
    )
  })
  
  output$valueboxes = renderUI({
    clean = function(x) {
      round(x,1) |> paste("°C")
    }
    
    layout_columns(
      value_box(
        title = "Average Temp",
        value = mean(d_city()$temp_avg, na.rm=TRUE) |> clean(),
        showcase = bsicons::bs_icon("thermometer-half"),
        theme = "success"
      ),
      value_box(
        title = "Minimum Temp",
        value = min(d_city()$temp_min, na.rm=TRUE) |> clean(),
        showcase = bsicons::bs_icon("thermometer-snow"),
        theme = "primary"
      ),
      value_box(
        title = "Maximum Temp",
        value = max(d_city()$temp_max, na.rm=TRUE) |> clean(),
        showcase = bsicons::bs_icon("thermometer-sun"),
        theme = "danger"
      )
    )
  })
  
  output$title = renderText({
    names(d_vars)[d_vars==input$var]
  })
  
  d_city = reactive({
    req(input$name)
    d |>
      filter(name %in% input$name)
  })
  
  output$plot = renderPlot({
    d_city() |>
      ggplot(aes(x=date, y=.data[[input$var]])) +
      geom_line() +
      theme_minimal()
  })
}

shinyApp(ui = ui, server = server)

Demo 09 - Some bslib Bells and Whistles

demos/demo09.R

library(tidyverse)
library(shiny)
library(bslib)

d = readr::read_csv(here::here("data/weather.csv"))

d_vars = c(
  "Average temp" = "temp_avg",   "Min temp"       = "temp_min",
  "Max temp"     = "temp_max",   "Total precip"   = "precip",
  "Snow depth"   = "snow",       "Wind direction" = "wind_direction",
  "Wind speed"   = "wind_speed", "Air pressure"   = "air_press"
)

ui = page_sidebar(
  title = "Weather Data",
  sidebar = sidebar(
    selectInput(
      "region", "Select a region", 
      choices = c("West", "Midwest", "Northeast", "South")
    ),
    selectInput(
      "name", "Select an airport", choices = c()
    ),
    
  ),
  card(
    card_header(
      textOutput("title"),
      popover(
        bsicons::bs_icon("gear", title = "Settings"),
        selectInput(
          "var", "Select a variable",
          choices = d_vars, selected = "temp_avg"
        )
      ),
      class = "d-flex justify-content-between align-items-center"
    ),
    card_body(
      plotOutput("plot")
    ),
    full_screen = TRUE
  ),
  uiOutput("valueboxes")
)

server = function(input, output, session) {
  observe({
    updateSelectInput(
      session, "name",
      choices = d |>
        distinct(region, name) |>
        filter(region == input$region) |>
        pull(name)
    )
  })
  
  output$valueboxes = renderUI({
    clean = function(x) {
      round(x,1) |> paste("°C")
    }
    
    layout_columns(
      value_box(
        title = "Average Temp",
        value = mean(d_city()$temp_avg, na.rm=TRUE) |> clean(),
        showcase = bsicons::bs_icon("thermometer-half"),
        theme = "success"
      ),
      value_box(
        title = "Minimum Temp",
        value = min(d_city()$temp_min, na.rm=TRUE) |> clean(),
        showcase = bsicons::bs_icon("thermometer-snow"),
        theme = "primary"
      ),
      value_box(
        title = "Maximum Temp",
        value = max(d_city()$temp_max, na.rm=TRUE) |> clean(),
        showcase = bsicons::bs_icon("thermometer-sun"),
        theme = "danger"
      )
    )
  })
  
  output$title = renderText({
    names(d_vars)[d_vars==input$var]
  })
  
  d_city = reactive({
    req(input$name)
    d |>
      filter(name %in% input$name)
  })
  
  output$plot = renderPlot({
    d_city() |>
      ggplot(aes(x=date, y=.data[[input$var]])) +
      geom_line() +
      theme_minimal()
  })
}

shinyApp(ui = ui, server = server)